Chapter 4
Data Management
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This is Florence Nightingale. She's known as an English social reformer and best known as the founder of modern nursing. She lived during the time of the Crimean War -- a war fought between the Russian Empire, and the alliance of the Ottoman Empire, France, United Kingdom, and Sardinia from 1853-1856.
She was also a statistician -- and did very well with it!
In 1854, British soldiers were dying in shocking numbers not mostly from battle wounds, but from disease. Hospitals were filthy, overcrowded, and badly managed. Everyone felt the situation was terrible, but no one could prove what was actually killing the soldiers.
To convince the government that hospitals needed attention, Nurse Florence meticulously collected data on causes of death: wounds, infections, sanitation-related diseases. Then she did something radical for her time: she visualized the data using what we now call polar area diagrams.
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The results were undeniable.
Her statistics showed that more than 60% of deaths were preventable, caused by poor sanitation rather than combat. Armed with numbers instead of opinions, she convinced the British government to reform military hospitals—improving ventilation, clean water, waste disposal, and hygiene.
The outcome?
Mortality rates dropped dramatically (from ~40% to under 5%).
Thousands of lives were saved.
Modern public health and hospital standards were born.
Here's the key lesson:
Statistics turned invisible suffering into life-saving truth.
Numbers can let us see truth.
No amount of emotion or anecdote could compete with carefully collected data, sound analysis, and clear presentation. Nightingale didn’t just treat patients—she changed systems, because statistics allowed her to see patterns others couldn’t and persuade people in power.
If you ever wonder why statistics matters, remember this:
It can expose hidden causes.
It can challenge false assumptions.
It can literally save lives.
Statistics is not just about numbers—it is about making reality visible.
Chapter Information
Chapter Information
Intended Learning Outcomes
Use a variety of statistical tools to process and manage numerical data.
Use the methods of linear regression and correlations to predict the value of a variable given certain conditions.
Advocate the use of statistical data in making important decisions.
Lectures
Lecture 12 - Measures of Central Tendency
Lecture 13 - Measures of Dispersion
Lecture 14 - Measures of Relative Position
Lecture 15 - Probability and the Normal Distribution
Lecture 16 - Correlation and Linear Regression
Lecture 17 - Basic Hypothesis Testing
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